一种面向业务承接的物流优化问题研究

传统的物流优化都是在运输公司已经承接了业务的前提下,通过优化方法求解最优运输方案.该文着重研究如何在众多运输任务中求取那些使运输公司获利最大的运输任务,进而判断承接哪些业务.在物流中心发布的运输业务和运输车辆比较少的情况下,决策者可以根据经验来判断承接哪些业务.但当运输业务和运输车辆比较多的时候,依靠经验来判断承接哪些业务已经不能保证决策的正确性.本文先对这种面向业务承接的物流优化问题进行建模,然后采用遗传算法求解,最后采用一个简单的例子进行了仿真验证.     

A continuous approach to considering uncertainty in facility design

This paper presents a formulation of the facilities block layout problem which explicitly considers uncertainty in material handling costs on a continuous scale by use of expected values and standard deviations of product forecasts. This formulation is solved using a genetic algorithm meta-heuristic with a flexible bay construct of the departments and total facility area. It is shown that depending on the attitude of the decision-maker towards uncertainty, the optimal design can change significantly. Furthermore, designs can be optimized directly for robustness over a range of uncertainty that is pre-specified by the user. This formulation offers a computationally tractable and intuitively appealing alternative to previous stochastic layout formulations that are based on discrete scenario probabilities.     

遗传算法的初步研究及改进后的遗传算法程序IGA1.0

遗传算法是近年来被广泛应用的一种非线性和并行算法。本文研究了几种改进遗传算法效率，提高搜索速度的方法，引入了两种变异的方法，并根据最大最小适应值的差值对适应值函数进行了修正，同时，对三种算子进行了重新安排以拓展搜索工在搜索过程中加入排序以提高杂交效率，同传统的遗传算法相 文的遗传算法没有使用固定的变异率和杂交率，而是让它们随着搜索过程中群体中的个体的重复情况改变，用经典的验证函数检验，这些改进提高     

An optimization algorithm for cutting stock problems in the TFT-LCD industry

Due to lack of efficient approaches of mixed production, the present production approach of the TFT-LCD industry is batch production that each glass substrate is cut into LCD plates of one size only. This study proposes an optimization algorithm for cutting stock problems of the TFT-LCD industry. The proposed algorithm minimizes the number of glass substrates required to satisfy the orders, therefore reducing the production costs. Additionally, the solution of the proposed algorithm is a global optimum which is different from a local optimum or a feasible solution that is found by the heuristic algorithm. Numerical examples are also presented to illustrate the usefulness of the proposed algorithm.     

An application of genetic algorithm methods for teacher assignment problems

The arrangement of courses at universities is an optimal problem to be discussed under multiple constraints. It can be divided into two parts: teacher assignments and class scheduling. This paper focused primarily on teacher assignments. Consideration was given to teacher's professional knowledge, teacher preferences, fairness of teaching overtime, school resources, and the uniqueness of the school's management. Traditional linear programming methods do not obtain satisfactory results with this complex problem.In this paper, genetic algorithm methods were used to deal with the issue of multiple constraints. As a global optimal searching method, the results of this study indicated that genetic algorithms can save significant time spent on teacher assignments and are more acceptable by the teachers.     

基于改进遗传算法的车辆路径问题求解

一直以来，车辆路径优化问题是物流系统中普遍受到关注的热点问题，也是一类算法比较复杂的问题。结合使用遗传算法和爬山法可以有效地提高解决这类复杂问题的效率，并可优化解的质量。     

用遗传算法进行工程优化的实现策略

本文针对常规遗传算法应用中容易产生早熟现象、效率不高和最优染色体编码易被破坏等问题,提出了解决这些问题的策略,并在计算机上编程予以实现。用这些策略解决了工程优化问题,结果证明,这些策略理论上是正确的,方法上是可行的。     

基于线杂交和面变异的遗传算法DGA

遗传算法由于其并行性和对全局信息的有效利用能力在化学和化工界得到越来越广泛的应用。但经典的跗算法在着一些缺点,如优化速度慢、空间搜索不均匀,搜索比较盲目等＾〖１〗。针对这些缺点,我们提出了结合均匀设计、有方向的搜索和遗传算法的确定性遗传算法ＤＧＡ,并用１８个经典测试函数和３个非线性规划问题对ＤＧＡ进行了测试。     

基于生产计划排单的遗传算法的优化与应用

ERP是当今国际上先进的企业管理模式,其核心是计划体系,包括主生产计划、采购计划、车间作业计划等。车间作业计划的管理目标是按物料需求计划的要求,按时、按质、按量和低成本地完成加工制造任务。该文探讨了遗传算法在车间作业计划安排中的应用,主要是针对Flow Shop的调度问题,给出了包括建模、编码、选择、交叉、变异和适应性函数等的具体算法,并在最后给出了自适应算法、混合遗传算法等优化算法。经仿真算例分析,该算法取得较理想的效果。     

Interactive genetic algorithms with individual’s fuzzy fitness

Interactive genetic algorithms are effective methods to solve an optimization problem with implicit or fuzzy indices, and have been successfully applied to many real-world optimization problems in recent years. In traditional interactive genetic algorithms, many researchers adopt an accurate number to express an individual’s fitness assigned by a user. But it is difficult for this expression to reasonably reflect a user’s fuzzy and gradual cognitive to an individual. We present an interactive genetic algorithm with an individual’s fuzzy fitness in this paper. Firstly, we adopt a fuzzy number described with a Gaussian membership function to express an individual’s fitness. Then, in order to compare different individuals, we generate a fitness interval based on α-cut set, and obtain the probability of individual dominance by use of the probability of interval dominance. Finally, we determine the superior individual in tournament selection with size two based on the probability of individual dominance, and perform the subsequent evolutions. We apply the proposed algorithm to a fashion evolutionary design system, a typical optimization problem with an implicit index, and compare it with two interactive genetic algorithms, i.e., an interactive genetic algorithm with an individual’s accurate fitness and an interactive genetic algorithm with an individual’s interval fitness. The experimental results show that the proposed algorithm is advantageous in alleviating user fatigue and looking for user’s satisfactory individuals.     

An improved exact algorithm for the domatic number problem

The 3-domatic number problem asks whether a given graph can be partitioned into three dominating sets. We prove that this problem can be solved by a deterministic algorithm in time n^2.695 (up to polynomial factors) and in polynomial space. This result improves the previous bound of n^2.8805, which is due to Björklund and Husfeldt. To prove our result, we combine an algorithm by Fomin et al. with Yamamoto's algorithm for the satisfiability problem. In addition, we show that the 3-domatic number problem can be solved for graphs G with bounded maximum degree Δ(G) by a randomized polynomial-space algorithm, whose running time is better than the previous bound due to Riege and Rothe whenever Δ(G)?5. Our new randomized algorithm employs Schöning's approach to constraint satisfaction problems.     

A genetic algorithm calibration method based on convergence due to genetic drift

The selection of Genetic Algorithm (GA) parameters is a difficult problem, and if not addressed adequately, solutions of good quality are unlikely to be found. A number of approaches have been developed to assist in the calibration of GAs, however there does not exist an accepted method to determine the parameter values. In this paper, a GA calibration methodology is proposed based on the convergence of the population due to genetic drift, to allow suitable GA parameter values to be determined without requiring a trial-and-error approach. The proposed GA calibration method is compared to another GA calibration method, as well as typical parameter values, and is found to regularly lead the GA to better solutions, on a wide range of test functions. The simplicity and general applicability of the proposed approach allows suitable GA parameter values to be estimated for a wide range of situations.     

用GA寻优线性系统模糊控制器规则

控制精度和自适应能力一直是模糊控制应用中较难解决的问题,解决这一问题的关键在于选取适当的控制规则,而遗传算法可以较好地解决常规的数学优化技术所不能有效解决的问题。该文给出了对于具有修正因子的控制规则,采用遗传算法对其参数进行自调整的方法,它可提高模糊控制器的性能。通过仿真实验表明了该方法对于线性系统的控制是有效的。     

A hybrid push/pull system in assemble-to-order manufacturing environment

A hybrid push/pull system of an assemble-to-order manufacturing environment is investigated in this paper. In this environment, raw material can be transformed into common semi-finished products at a point where next downstream operations are triggered by customer orders. The production of the earlier upstream stations is controlled by push-type production, while the production of the later downstream stations is controlled by pull-type production. The hybrid system often compromises the conflicting performance characteristics of the push and the pull environments. In the push type, high inventory cost is anticipated in the return of low delivery leadtime. On the contrary, in the pull type, high delivery leadtime is expected in the return of low inventory cost. The objective function for the presented hybrid model is to minimize the sum of inventory holding cost and delivery leadtime cost, which is the cost of the time period since customers have placed an order until it is fulfilled. The model is applied to solve the inventory and late delivery problems in an assemble-to-order manufacturer. A genetic algorithm (GA) is used. A discrete event simulation model is used to evaluate the objective function for each chromosome in the GA. The pure push and pull systems are also simulated in order to compare their performance with the hybrid system. Sensitivity analysis on the coefficient of variation (CV) of time between actual customer order arrivals and on various cost ratios of delivery leadtime and inventory are carried out. In most cases, the hybrid performs the best. Results show that the hybrid production system would save the company significantly compared to the pure push or pure pull production systems.     

Evolutionary optimization of a geometrically refined truss

Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection, and stress on a benchmark cantilever truss problem. Predominantly, traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This paper will present a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation—genetic algorithms and differential evolution—to successfully optimize a benchmark structural optimization problem. A nontraditional solution to the benchmark problem is presented in this paper, specifically, a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization tool set.